Kalman filtering algorithm for self-similar traffic prediction

A noise on-line estimation Kalman filtering (NOEKF) algorithm is presented to deal with the inaccurate self-similar traffic prediction in network congestion control. The proposed algorithm is independent of the feedback information from traffic sources, and predicts the traffic through observing both the current and previous traffics in a node. Both the state equation and the observation equation are established, and then an optimal recursive formula for the estimation of the state vector is given. By taking the unknown noise statistics of both the state equation and the observation equation into account, an on-line estimation method with forgetting factor is used to estimate the noise statistics. Comparisons with existing algorithms show that the NOEKF algorithm has the advantages of high accuracy and minor prediction error. Simulation results show that the NOEKF algorithm predicts self-similar traffic more accurately than the classical Kalman filtering and time series prediction algorithms do, and that the prediction error is reduced by more than 60%.